Universal Wall Boundary Condition Treatment for K-Omega Turbulence Models

ABSTRACT

Disclosed are techniques for simulating a physical process and for determining boundary conditions for a specific energy dissipation rate of a k-Omega turbulence fluid flow model of a fluid flow, by computing from a cell center distance and fluid flow variables a value of the specific energy dissipation rate for a turbulent flow that is valid for a viscous layer, buffer layer, and logarithmic region of a boundary defined in the simulation space. The value is determined by applying a buffer layer correction factor as a first boundary condition for the energy dissipation rate and by applying a viscous sublayer correction factor as a second boundary condition for the energy dissipation rate.

BACKGROUND

This description relates to computer simulation of physical processes,such as physical fluid flows.

High Reynolds number flow has been simulated by generating discretizedsolutions of the Navier-Stokes differential equations by performinghigh-precision floating point arithmetic operations at each of manydiscrete spatial locations on variables representing the macroscopicphysical quantities (e.g., density, temperature, flow velocity). Oneapproach simulate a problem of interest uses the so called SST k-Omegaturbulence model, a widely used turbulence model, which solves twopartial differential equations (k) and (Omega). One of these equations(k) is for turbulent kinetic energy (TKE) and one of these equations(Omega) is for specific energy dissipation rate (SEDR). The combinationof these equations define a turbulent state of a fluid flow.

In principle, it is possible to solve the fluid dynamics governingequations (e.g., the Navier-Stokes equations) to simulate the problem ofinterest. Unfortunately, most practical engineering problems involveflow conditions that occur at large Reynolds number flows underturbulent flow conditions. This requires the Computational Fluid Dynamic(CFD) simulations solvers to apply turbulence modeling technology toreduce the time and computational model size to practical levels, inorder to obtain solutions under such conditions with reasonablecomputational and time costs. Turbulence modeling is a methodology thatapproximates the behavior of turbulent fluid flows.

One such model is the shear stress transport formulation of a k-Omegaturbulence model or simply “SST k-Omega turbulence model.” The SSTk-Omega turbulence model involves the solution to two partialdifferential equations. As is generally true of partial differentialequations, the k and the Omega partial differential equations requirespecifications of boundary conditions in order for those equations to besolved.

While the equation for the turbulent kinetic energy (k) is subject toDirichlet and Neumann boundary conditions that define integration ofthis equation with the specific energy dissipation rate (the Omegapartial differential equation), the Omega partial differential equationlacks boundary conditions of either the Dirichlet or the Neumann type atwall boundaries, and as such the Omega partial differential equationcannot be readily integrated with the k partial differential equation toprovide a solution.

In order to provide a solution that effectively integrates theseequations, typically a near-wall analysis is conducted to simplify theOmega partial differential equation to derive an ancillary equation thatdescribes the behavior of the Omega partial differential equation as thewall-boundary is asymptotically approached.

However, while this auxiliary equation is not a formal boundarycondition that can be used to solve the Omega partial differentialequation, enforcing this equation close to a wall boundary neverthelessallows for a solution. The problem is how to enforce the auxiliary toequation. Various solutions have been proposed. One proposed solution toenforce the auxiliary equation behavior produces additional mesh pointsto recover asymptotic behavior. Another solution proposed is amodification to the auxiliary equation to enforce the auxiliary equationas a Dirichlet boundary condition.

SUMMARY

As mentioned above a drawback of the specific SST k-Omega turbulencemodel and more generally of k-Omega models is that the Omega partialdifferential equation for the specific energy dissipation rate lacksboundary conditions of either the Dirichlet or the Neumann type at thewall boundaries, and as such cannot be readily integrated with the kequation to provide a solution.

Described herein is a generalized wall-boundary condition treatment forthe Omega partial differential equation for use in specifying boundaryconditions with simulations that use the SST k-Omega turbulence modelspecifically, and more generally, the solution is applicable to k-Omegaturbulence model formulations in general.

The generalized wall-boundary condition described herein automaticallyimposes correct auxiliary asymptotic relationships needed in theviscous-sublayer and logarithmic-layer and thus eliminates pathologicalmesh-dependent degradation in accuracy that is exhibited when theviscous-sublayer is not properly resolved by the mesh. The describedgeneralized wall-boundary condition can eliminate the pathologicalmesh-dependent degradation in accuracy exhibited when the first elementaway from the wall boundary is located within the buffer layer. Inaddition, the generalized wall-boundary condition treatment canaccurately predict results independently of the location of the firstelement away from the wall provided that the rest of the boundary layeris properly resolved.

According to an aspect, a computer-implemented method for simulatingfluid flow about a simulated physical object includes receiving by oneor more computing systems, a model of a simulation space that includes amesh defining a representation of the physical object in the simulationspace, with the mesh comprising plural cells having resolutions toaccount for surfaces of the physical object, determining boundaryconditions for a specific energy dissipation rate of a k-Omegaturbulence fluid flow model of the fluid flow, by determining a cellcenter of a cell in the mesh and computing by the one or more computingsystems from the cell center distance and fluid flow variables a valueof the specific energy dissipation rate for a turbulent flow that isvalid for a viscous layer, buffer layer, and logarithmic region of aboundary defined in the simulation space.

Other aspects include computing systems and computer program products.

One or more of the above aspects can include any one or more of thefollowing features or other features as disclosed below.

For a cell located at a position y+<3 of the boundary where y is a cellat the boundary, the aspects apply a buffer layer correction factor as aboundary condition for the energy dissipation rate. Determining theboundary conditions includes applying by the one or more computingsystems, a buffer layer correction factor as a boundary condition forthe energy dissipation rate for a cell located at a position y+<3 of theboundary where y is a cell at the boundary, and the correction factor isgiven according to:

ω′_(Hyb)=f_(blend)ω_(Hyb)

where ω′_(Hyb) is the correction factor f_(blend) is a blending functionand ω_(Hyb) is a viscus layer correction function

Determining the boundary conditions includes applying a viscous sublayercorrection factor as a boundary condition for the energy dissipationrate. Determining the boundary conditions further includes applying bythe one or more computing systems, a viscous sublayer correction factoras a boundary condition for the energy dissipation rate, with theviscous sublayer correction factor given according to:

$\frac{\partial\omega_{v}^{f}}{\partial y} = {\frac{\partial\omega_{v}^{d}}{\partial y}\left\lfloor \frac{y_{1}^{2}y_{2}^{2}}{\left( \frac{y_{2} + y_{1}}{2} \right)^{4}} \right\rfloor}$${where}\mspace{14mu} \frac{\partial\omega_{v}^{f}}{\partial y}$

is the correction factor, y₁ ² is the cell at location 1 and y₂ ² is thecell at position 2.

The aspects further include accessing the k-Omega model, initializingthe accessed k-Omega model with the determined boundary conditions, andexecuting the initialized k-Omega model to simulated the fluid flowabout the simulated physical object. The aspects further includeaccessing the k-Omega turbulence fluid flow model, with the k-Omegaturbulence fluid flow model, including a first partial differentialequation to determine turbulent kinetic energy of the fluid flow; and asecond partial differential equation to determine the specific energydissipation rate of the fluid flow in the simulation space.

The aspects further include determining whether the location is at abuffer layer; and when at a buffer layer, applying a correction thatincrease the values of energy dissipation rate only at the buffer layer.Applying the correction further includes applying a blending functionthat acts on the values of energy dissipation rate at the buffer layerand prevents the blending function to affect values at the viscous layerof the boundary defined in the simulation space.

One or more of the above aspects may provide one or more of thefollowing advantages.

The approach to specifying boundary conditions may substantially improvethe accuracy of turbulent CFD simulations that use any family of k-Omegamodels, relative to current boundary condition strategies. The approachremoves from predictions mesh-dependency found on meshes that do notproperly resolve the viscous-sublayer, unlike current boundary conditionstrategies. The approach removes or substantially reducesmesh-dependency on meshes whose first grid element away from the wall,resides at the buffer layer, unlike current boundary conditionstrategies. The approach provides a boundary condition treatmentthroughout the inner-layer of the turbulent boundary layer that isindependent of the location of the first grid point away from the wall,unlike current boundary condition strategies. The approach maygeneralize the application of boundary conditions for the Omega partialdifferential equation at the wall, enforcing the viscous-sublayer andlogarithmic-layer asymptotic behaviors, unlike current boundarycondition strategies.

BRIEF DESCRIPTION OF THE DRAWINGS

FIG. 1 depicts a system for simulation of fluid flows, which includes aturbulent boundary layer model for compressible or incompressible flows.

FIG. 2 depicts a flow chart showing operations for formulation of aK-Omega SST turbulence model.

FIG. 3 depicts a flow chart showing operations for correction todetermining boundary conditions.

FIG. 4 is a plot that illustrates a solution of the Omega PDE in aturbulent channel flow.

FIG. 5 is a plot that illustrates blending of ancillary relationshipsfor Omega at the viscous sublayer and logarithmic layer.

FIG. 6 is a plot that illustrates mesh dependency according to viscoussublayer resolution.

FIG. 7 is a plot that illustrates distribution of Omega and channel flowwhen near wall resolution is not sufficiently fine in gradation.

FIG. 8 is a diagram that illustrates two cells close to a boundary.

FIG. 9 is a plot that illustrates a mean velocity profile and frictionfactor for different near-wall resolutions (uncorrected), with pluralexamples at resolution cases plotted, but only selective ones indicatedby a reference line for clarity.

FIG. 10 is a plot that illustrates a mean velocity profile and frictionfactor for different near-wall resolutions (corrected), with pluralexamples at resolution cases plotted, but only selective ones indicatedby a reference line for clarity.

FIG. 11 is a plot that illustrates mismatch of Omega in the bufferlayer.

FIG. 12 is a plot that illustrates performance of boundary layerconditions in the buffer layer, with plural examples at resolution casesplotted, but only selective ones indicated by a reference line forclarity.

FIG. 13 is a plot that illustrates a blending function that improvesaccuracy in the buffer layer.

FIG. 14 is a plot that illustrates correction of omega in the bufferlayer.

FIG. 15 is a plot that illustrates results of applying a boundarycondition correction, with plural examples at resolution cases plotted,but only selective ones indicated by a reference line for clarity.

DETAILED DESCRIPTION

Described below is a generic auxiliary equation that is derived andwhich correctly reproduces near wall behavior and wall-function limitswith a correction that eliminates mesh-dependent solutions in thenear-wall limit to provide a practical implementation of the auxiliaryboundary condition treatment of the Omega partial differential equationin k-Omega turbulence models used in computation fluid dynamicsimulations.

In the discussion below, a boundary condition treatment is introducedfor the Omega partial differential equation that is applicable to allthe k-Omega turbulence models. In the discussion below, distance from awall boundary is referred to as “y+n,” where “y” is a boundary and “n”is a number of cells away from the boundary, where n is a number thattakes as a value of either an integer or a decimal.

The boundary condition approach discussed below references: a viscoussub-layer nominally between y+0.1 to y+5; a logarithmic-layer (loglayer) that is a fully turbulent region away from the wall, e.g., y+>25;and a buffer layer that is neither the viscous sublayer nor the fullyturbulent logarithmic region and is typically defined by 5<y+<25. Othervariations in these ranges are possible.

The boundary condition approach is based on deriving a universalauxiliary relationship for ω that describes the asymptotic behaviors ofthe Omega partial differential equation in the viscous sub-layer (e.g.,a layer that is close to the wall boundary, typically y+5)ω_(v), andthat is fully compliant with a turbulent equilibrium logarithmic-layer(e.g., a layer distal from the wall, typically y+25, but still withinthe boundary layer), ω_(L). This approach to the auxiliary equation alsoincludes a correction factor that fixes a degradation in accuracyassociated with relatively poor mesh-resolution in the viscous-sublayer.

Referring now to FIG. 1, a system 10 includes a simulation engine 34configured for CFD simulations using any model of the family of k-Omegaturbulence models. The simulation engine 34 includes a turbulence modelmodule 34 a that executes a specific k-Omega turbulence model, and aboundary module 34 b that automatically specifies boundary conditionsfor use with the turbulence model module 34 a. The boundary conditionsexecuted in the boundary module 34 b are derived from a universalsolution for co that satisfies turbulent behavior within a boundarylayer. In other words, an auxiliary relationship is provided thatdescribes asymptotic behaviors of the Omega partial differentialequation in a viscous sub-layer (e.g., a layer that is close to the wallboundary, e.g., y+5), while being fully compliant with a turbulentequilibrium logarithmic layer (e.g., a layer distal from the wall, e.g.,y+25, yet still within the boundary layer).

The system 10 in this implementation is based on a client-server orcloud based architecture and includes a server system 12 implemented asa massively parallel computing system 12 (stand alone or cloud-based)and a client system 14. The server system 12 includes memory 18, a bussystem 11, interfaces 20 (e.g., user interfaces/networkinterfaces/display or monitor interfaces, etc.) and a processing device24. In memory 18 are a mesh preparation engine 32 and a simulationengine 34.

While FIG. 1 shows the mesh preparation engine 32 in memory 18, the meshpreparation engine can be a third party application that is executed ona different system than server 12. Whether the mesh preparation engine32 executes in memory 18 or is executed on a different system thanserver 12, the mesh preparation engine 32 receives a user-supplied meshdefinition 30 and the mesh preparation engine 32 prepares a mesh andsends (and or stores) the prepared mesh to the simulation engine 34according to a physical object that is being modelled for simulation bythe simulation engine 34. The system 10 accesses a data repository 38that stores 2D and/or 3D meshes (Cartesian and/or curvilinear),coordinate systems, and libraries. Any of number physical objects orphysical fluid flows can be represented in a simulation space that isused to represent the physical object or physical flow. Simulations canbe used in a broad range of technical and engineering problems includingaerodynamic/aerospace analysis, weather, environmental engineering,industrial systems, biological systems, general fluid flows, andcombustion systems, etc.

Referring now to FIG. 2, a process 40 for simulating fluid flow about arepresentation of a physical object is shown. In the example that willbe discussed herein, the physical object is an airfoil. The use of anairfoil is merely illustrative however, as the physical object can be ofany shape, and in particular can have planar and/or curved surface(s).The process 40 receives 42, e.g., from client system 14 or retrievesfrom the data repository 38, a mesh (or grid) for the physical objectbeing simulated. In other embodiments, either an external system or theserver 12 based on user input, generates the mesh for the physicalobject being simulated. Other examples include

The process precomputes 44 geometric quantities from the retrieved mesh.Process 40 determines 46 boundary conditions (e.g., in the boundarymodule 34 b) based on the retrieved mesh and flow field variables. Flowfield variables such as velocity, Omega, k, temperature, etc., are usedto compute ω_(HCorr) for the object being simulated for use in theboundary module 34 b. Once the turbulence variables are known, thepartial differential equations k and Omega are solved. The turbulencemodel provides eddy viscosity which represents the effects of turbulencethat cannot be resolved by the mesh and is used to advance the solution.The mesh comprises or divides the simulation space into plural cells.

The process performs 48 the computational fluid dynamic simulation (CFD)using a selected k-Omega turbulence model. The model is initialized bythe boundary conditions and is executed in turbulence model module 34 a,using the precomputed geometric quantities corresponding to theretrieved mesh.

Referring now to FIG. 3, a process 60 for simulating fluid flow about asimulated physical object is shown. The process 60 includes receiving 62by a computing systems, a model of a simulation space that includes amesh defining a representation of the physical object in the simulationspace, with the mesh comprising plural cells having resolutions toaccount for surfaces of the physical object. The process 60 alsoincludes determining 64 boundary conditions for a specific energydissipation rate of a k-Omega turbulence fluid flow model of the fluidflow, by determining a cell center of a cell in the mesh and computing66 by the one or more computing systems from the cell center distanceand fluid flow variables a value of the specific energy dissipation ratefor a turbulent flow that is valid for a viscous layer, buffer layer,and logarithmic region of a boundary defined in the simulation space.

The process 60 also includes for a cell located at a position y+<3 ofthe boundary where y is a cell at the boundary, applying 68 a a bufferlayer correction factor as a boundary condition for the energydissipation rate. The process 60 also includes applying 69 b a viscoussublayer correction factor as a boundary condition for the energydissipation rate.

In order to derive the wall-function assumptions, discussed above, thefluid flow is analyzed over a turbulent channel flow. The Reynoldsaveraged governing equations for an incompressible turbulent channel aregiven by:

$\begin{matrix}{{\frac{{\partial\rho}u_{i}}{\partial t} + \frac{{\partial\rho}\; u_{j}u_{i}}{\partial x_{j}}} = {{- \frac{\partial p}{\partial x_{i}}} + {\frac{\partial}{\partial x_{j}}\left\lbrack {\tau_{ij} - \overset{\_}{\rho \; u_{i}^{\prime}u_{j}^{\prime}}} \right\rbrack}}} & \left( {1.a} \right) \\{\frac{\partial u_{j}}{\partial x_{j}} = 0} & \left( {1.b} \right)\end{matrix}$

For fully developed flows and no mean span wise velocity, theassumptions are:

$\begin{matrix}{{w = 0},{\frac{\partial}{\partial x}{= {\frac{\partial}{\partial z}{= 0}}}}} & (2.0)\end{matrix}$

The x and z components of a turbulent flow are 0 means that that onaverage the flow does not change over x and z. However, over thosedirections, the flow can change instantaneously over portions of ageometric space.

Therefore, the equation for continuity becomes

$\begin{matrix}{{\frac{\partial v}{\partial x} = 0},{v = c},{{v\left( {y = 0} \right)} = 0},{v = 0}} & (3.0)\end{matrix}$

This implies that advection is zero, and thus the steady state governingequations are as:

$\begin{matrix}{\frac{\partial p}{\partial x_{i}} = {\frac{\partial}{\partial x_{j}}\left\lbrack {\tau_{ij} - \overset{\_}{\rho \; u_{i}^{\prime}u_{j}^{\prime}}} \right\rbrack}} & (4.0)\end{matrix}$

Evaluating the equation in the Z direction yields

$\begin{matrix}{{{\frac{\partial p}{\partial z} = {\frac{\partial}{\partial y}\left\lbrack {\tau_{zy} - \overset{\_}{\rho \; w^{\prime}v^{\prime}}} \right\rbrack}},{\tau_{zy} = {{\mu( + )} = 0}}}{\frac{\partial p}{\partial z} = {{- \frac{\partial}{\partial y}}\left( \overset{\_}{\rho \; w^{\prime}v^{\prime}} \right)}}} & (5.0)\end{matrix}$

This equation balances throughout the Z axis, which means that thegradient of the Reynolds stress is constant and symmetric along the Zaxis.

$\begin{matrix}{\frac{\partial p}{\partial z} = {{{- \frac{\partial}{\partial y}}\left( {\overset{\_}{\rho \; w^{\prime}v^{\prime}}(z)} \right)} = {{- \frac{\partial}{\partial y}}\left( {\overset{\_}{\rho \; w^{\prime}v^{\prime}}\left( {- z} \right)} \right)}}} & \left( {6.a} \right)\end{matrix}$

This implies that the Reynolds stress is antisymmetric

ρw′v′ (z)=−ρw′v′ (−z)   (6.b)

Thus the Reynolds stress at z=0 is zero

$\begin{matrix}{{\frac{\partial p}{\partial z} = 0},{{p(z)} = {cte}},{p = {p\left( {x,y,{z = 0}} \right)}}} & \left( {6.c} \right)\end{matrix}$

This means that the pressure does not change over the Z axis and issufficient enough to “measure” the pressure at the center of thespan-wise direction z=0. Evaluating the equation in the Y directionstates that the wall-normal Reynolds stress is only function of thewall-normal direction.

$\begin{matrix}{{{\frac{\partial p}{\partial y} = {\frac{\partial}{\partial y}\left\lbrack {\tau_{yy} - \overset{\_}{\rho \; v^{\prime}v^{\prime}}} \right\rbrack}},{\tau_{zy} = {{\mu } = 0}}}{\frac{\partial p}{\partial y} = {{- \frac{\partial}{\partial y}}\left( \overset{\_}{\rho \; v^{\prime}v^{\prime}} \right)}}} & \left( {5.a} \right)\end{matrix}$

This can be integrated to yield

p=−ρv′v′+cte

p(y)=−ρv′v′ (y)+p_(wall)   (5.b)

From this equation, the pressure gradient in the stream-wise directionis equal to the gradient of the pressure at the wall

$\begin{matrix}{{\frac{\partial p}{\partial x} = {\frac{\partial}{\partial x}\left( {{{- \overset{\_}{\rho \; v^{\prime}v^{\prime}}}(y)} + p_{wall}} \right)}}{\frac{\partial p}{\partial x} = \frac{\partial p_{wall}}{\partial x}}} & \left( {5.c} \right)\end{matrix}$

Evaluating the X-momentum equation using Eq.(5.c) and applying symmetryconditions yields

$\begin{matrix}{{\frac{\partial p_{wall}}{\partial x} = {\frac{\partial}{\partial y}\left( {\tau_{xy} - \overset{\_}{{pu}^{\prime}v^{\prime}}} \right)}},{\tau_{xy} = {\mu\left( {\frac{\partial u}{\partial y} +} \right)}},{{\frac{\partial u}{\partial y}(y)} = {{- \frac{\partial u}{\partial y}}\left( {- y} \right)}},{{\overset{\_}{{pu}^{\prime}v^{\prime}}(y)} = {{- \overset{\_}{{pu}^{\prime}v^{\prime}}}\left( {- y} \right)}},{\frac{\partial p_{wall}}{\partial x} = {\frac{\partial}{\partial y}\left( {\frac{\partial u}{\partial y} - \overset{\_}{{pu}^{\prime}v^{\prime}}} \right)}}} & \left( {6.a} \right) \\{{{y\frac{\partial p_{wall}}{\partial x}} = {{\mu \frac{\partial u}{\partial y}} - \overset{\_}{{pu}^{\prime}v^{\prime}} + {cte}}},{{\overset{\_}{{pu}^{\prime}v^{\prime}}\left( {y = 0} \right)} = 0},{{cte} = {- \tau_{wall}}}} & \left( {6.b} \right)\end{matrix}$

Evaluating this equation at the center of the channel y=H yields

$\begin{matrix}{{{y\frac{\partial p_{wall}}{\partial x}} = {{\mu \frac{\partial u}{\partial y}} - \overset{\_}{{pu}^{\prime}v^{\prime}} - \tau_{wall}}}{{{H\frac{\partial p_{wall}}{\partial x}} = {- \tau_{wall}}},}} & \left( {7.a} \right)\end{matrix}$

Thus the pressure drop in the channel can be computed as

$\begin{matrix}{{\Delta \; p} = {{- \tau_{wall}}\frac{L}{H}}} & \left( {7.b} \right)\end{matrix}$

The momentum equation can also be written in terms of the total stress

$\begin{matrix}{{\frac{\partial p_{wall}}{\partial x} = {\frac{\partial}{\partial y}\left( \tau_{tot} \right)}},{\tau_{tot} = {\tau_{xy} - \overset{\_}{{pu}^{\prime}v^{\prime}}}},{{\tau_{tot}\left( {y = H} \right)} = 0},{{\tau_{tot}\left( {y = 0} \right)} = \tau_{wall}},{{y\frac{\partial p_{wall}}{\partial x}} = {\tau_{tot} + {cte}}},{{y\frac{\partial p_{wall}}{\partial x}} = {\tau_{tot} - \tau_{wall}}},} & \left( {7.c} \right)\end{matrix}$

Using Eq.(7.a) equation (7.c) can be written as

$\begin{matrix}{{\tau_{tot} = {\tau_{wall}\left( {1 - \frac{y}{H}} \right)}},{{Re}_{\tau} = \frac{\rho \; u_{\tau}H}{\mu}}} & \left( {7.c} \right)\end{matrix}$

Therefore, the total stress in the channel can be written as function ofy+(Eq.7d), and the Friction Reynolds numbers (Eq. 8.0).

$\begin{matrix}{{\tau_{tot} = {\tau_{wall}\left( {1 - \frac{y^{+}}{{Re}_{\tau}}} \right)}},} & (8.0)\end{matrix}$

If this equation is expanded and the so called “law-of-the-wall” is usedto compute the velocity gradients Eq.(8.0) can be written as:

$\begin{matrix}{{{{\mu \frac{\partial u}{\partial y}} - \overset{\_}{{pu}^{\prime}v^{\prime}}} = {\tau_{wall}\left( {1 - \frac{y^{+}}{{Re}_{\tau}}} \right)}},} & \left( {9.a} \right)\end{matrix}$

where the law of the wall conveys is an universal function that relatesthe value of the mean velocity profile with y+, this velocity profile iscomposed by three regions, the viscous sublayer (where the velocityfollows a linear function for y+<5), the logarithmic layer (where thevelocity follows a logarithmic function for y+>25), and the buffer layerwhich is a smooth transition function between both layers for^(5<)y+<25, and at the wall becomes:

$\begin{matrix}{{{{\tau_{wall}\frac{\partial u^{+}}{\partial y^{+}}} - \overset{\_}{{pu}^{\prime}v^{\prime}}} = {\tau_{wall}\left( {1 - \frac{y^{+}}{{Re}_{\tau}}} \right)}},} & \left( {9.b} \right)\end{matrix}$

So the Reynolds stresses can be exactly computed

$\begin{matrix}{{{- \overset{\_}{\rho \; u^{\prime}v^{\prime}}} = {\tau_{wall}\left( {1 - \frac{y^{+}}{{Re}_{\tau}} - \frac{\partial u^{+}}{\partial y^{+}}} \right)}},} & \left( {9.c} \right)\end{matrix}$

The flow starts becoming turbulent at Re, >100, so y⁺<5, u⁺=y⁺ can bewritten for the viscous-sublayer as:

$\begin{matrix}{{{- \overset{\_}{\rho \; u^{\prime}v^{\prime}}} = {\tau_{wall}\left( {1 - \frac{y^{+}}{{Re}_{\tau}} - 1} \right)}},{{{- \overset{\_}{\rho \; u^{\prime}v^{\prime}}} - \frac{\tau_{wall}y^{+}}{{Re}_{\tau}}} \approx 0}} & \left( {10.a} \right)\end{matrix}$

This shows that the Reynolds stress is about zero in theviscous-sublayer. In the logarithmic region y⁺>25,

${u^{+} = {{\frac{1}{\kappa}{\ln \left( y^{+} \right)}} + B}},$

the Reynolds stress is approximated as

$\begin{matrix}{{{- \overset{\_}{\rho \; u^{\prime}v^{\prime}}} = {\tau_{wall}\left( {1 - \frac{y^{+}}{{Re}_{\tau}} - \frac{1}{\kappa \; y^{+}}} \right)}},{{- \overset{\_}{\rho \; u^{\prime}v^{\prime}}} \approx \tau_{wall}}} & \left( {10.b} \right)\end{matrix}$

Therefore, for large enough Re_(τ) the Reynolds stress is equal to thewall-shear stress. Moreover, even for a Reynolds stress Re_(τ)=100 , inthe logarithmic region y⁺=25, the equation (10.b) provides−ρu′v′=0.6524τ_(wall)≈τ_(wall), which is barely turbulent, and forlarger Reynolds number −ρu′v′=0.9τ_(wall)≈τ_(wall), approximately equalto turbulence at the wall or boundary.

The implementation of the production close to the wall is done applyingEq. (9.c) by assuming proximity being very close to the wall andeliminating the second term, which for high Re_(τ) should be zero.

$\begin{matrix}{{{Pk} = {{{- \overset{\_}{\rho \; u^{\prime}v^{\prime}}}\frac{\partial u}{\partial y}} \simeq {{\tau_{wall}\left( {1 - \frac{\partial u^{+}}{\partial y^{+}}} \right)}\frac{\partial u}{\partial y}}}},{{Pk} \simeq {\frac{\tau_{wall}^{2}}{\mu}\left( {1 - \frac{\partial u^{+}}{\partial y^{+}}} \right){\frac{\partial u^{+}}{\partial y^{+}}.}}}} & \left( {10.d} \right)\end{matrix}$

It is possible to relate the wall-shear stress with the turbulencevariables. A turbulent kinetic energy budget predicts that in thelogarithmic region production balances dissipation

$\begin{matrix}{{{{- \overset{\_}{\rho \; u^{\prime}v^{\prime}}}\frac{\partial u}{\partial y}} = {\rho ɛ}},} & (11.0)\end{matrix}$

Using the k−ε model the eddy viscosity is written as

$\begin{matrix}{\mu_{T} = {{pC}_{\mu}\frac{k^{2}}{ɛ}}} & (12.0)\end{matrix}$

Using Eq.(12.0) in (11.0) the wall-shear can be written in terms of theturbulent kinetic energy in the logarithmic layer.

$\begin{matrix}{{{{- \overset{\_}{\rho \; u^{\prime}\; v^{\prime}}}\frac{\partial u}{\partial y}} = {\rho^{2}\frac{C_{\mu}k^{2}}{\mu_{T}}}},{{- {\overset{\_}{\rho \; u^{\prime}\; v^{\prime}}}^{2}} = {\rho^{2}C_{\mu}k^{2}}},{\tau_{wall}^{2} = {\rho^{2}C_{\mu}k^{2}}}} & (13.0)\end{matrix}$

So the friction velocity

$u_{\tau} = \sqrt{\frac{\tau_{wall}}{\rho}}$

can be written as

u _(τ) =C _(μ) ^(1/4) k ^(1/2)   (14.a)

To compute the values of s in the viscous sublayer and logarithmicregion the following relationships can be used. Based on the TKE budgetat the wall the diffusion of TKE balances the energy-dissipation rate.

$\begin{matrix}{{v\frac{\partial^{2}k}{\partial y^{2}}} = ɛ} & \left( {14.b} \right)\end{matrix}$

This can be integrated as

$\begin{matrix}{k = {\frac{ɛy^{2}}{2v} + {C_{1}y} + C_{2}}} & (15.0)\end{matrix}$

Given the boundary condition k (y=0)=0, C₂=0, the turbulent kineticenergy can be written as

$\begin{matrix}{k = {y\left( {\frac{ɛ\; y}{2\nu} + C_{1}} \right)}} & (16.0)\end{matrix}$

Since the turbulent kinetic energy has to always be positive thisrequires that

$\begin{matrix}{{C_{1} \geq {- \frac{ɛy}{2v}}},{\forall y}} & (17.0)\end{matrix}$

Further information can be obtained by looking for the location of thecritical point, since this point exists and is a minimum by Eq. (14.b).

$\begin{matrix}{{\frac{\partial k}{\partial y} = {{\frac{ɛ\; y}{\nu} + C_{1}} = 0}},{y = {- \frac{C_{1^{V}}}{ɛ}}}} & (18.0)\end{matrix}$

Since the critical point has to be located within the domain y≥0, thisimposes that C₁₂≤0. Imposing both conditions yields

$\begin{matrix}{{C_{1} \geq {- \frac{ɛy}{2v}}},{{\&\mspace{14mu} C_{1}} \leq {0{\forall y}}}} & (19.0)\end{matrix}$

Since this holds for all y including y=0, conditions in Eq.(19.0) become

C₁≥0, & C₁≤0   (20.0)

The only way that equation 20.0 can be true is when C₁ =0, which impliesthat

$\begin{matrix}{{ɛ = \frac{2vk}{y^{2}}},{\left. \frac{\partial k}{\partial y} \right|_{wall} = 0}} & (21.0)\end{matrix}$

The relationship for the dissipation rate in the logarithmic region canbe obtained by substituting Eq.(10.b) on Eq.(11.0)

$\begin{matrix}{ɛ = {{\tau_{wall}\frac{u_{\tau}^{2}}{\mu}\frac{\partial u^{+}}{\partial y^{+}}} = {{\tau_{wall}\frac{u_{\tau}^{2}}{\mu \kappa y^{+}}} = \frac{u_{\tau}^{3}}{\kappa y}}}} & (22.0)\end{matrix}$

and by using Eq.(14.a), the logarithmic relationship can be written as

$\begin{matrix}{ɛ = \frac{C_{\mu}^{3/4}k^{3/2}}{\kappa \; y}} & (23.0)\end{matrix}$

If eddy viscosity is evaluated using Eq.(23.0), the eddy viscosity canbe found to be linear in the logarithmic region.

v _(T) =u _(τ) κy   (24.0)

Near-wall Turbulence Analysis for Plane Channel-Flows

Results that are predicted by the SST k-Omega model have a near-walldependency on the results. This dependency is due to the near-wallbehavior of the Omega (ω) equation when it approaches the near wallregion.

Taking the original Model:

$\begin{matrix}{{\frac{{\partial\rho}k}{\partial t} + \frac{{\partial\rho}\; u_{j}k}{\partial x_{j}}} = {{2\tau_{ij}S_{ij}} - {\beta^{*}{\rho\omega}\; k} + {\frac{\partial}{\partial x_{j}}\left\lbrack {\left( {\mu + {\sigma_{k}\mu_{t}}} \right)\frac{\partial k}{\partial x_{j}}} \right\rbrack}}} & \left( {25.a} \right)\end{matrix}$

$\begin{matrix}{{\frac{{\partial\rho}\omega}{\partial t} + \frac{{\partial\rho}\; {u\;}_{j}\omega}{\partial x_{j}}} = {{\frac{\gamma}{v_{T}}2\tau_{ij}S_{ij}} - {\beta \; \rho \; \omega^{2}} + {\frac{\partial}{\partial x_{j}}\left\lbrack {\left( {\mu + {\sigma_{\omega}\mu_{t}}} \right)\frac{\partial\omega}{\partial x_{j}}} \right\rbrack} + {2\left( {1 - F_{1}} \right){\rho\sigma}_{\omega 2}\frac{1}{\omega}\frac{\partial k}{\partial x_{j}}\frac{\partial\omega}{\partial x_{j}}}}} & \left( {25.b} \right)\end{matrix}$

For steady flows very close to the wall Eq.(1.b) can be simplified to

$\begin{matrix}{{v\frac{\partial^{2}\omega}{\partial y^{2}}} = {\beta \omega^{2}}} & (26.0)\end{matrix}$

This equation has the following solution only applicable very close tothe wall

$\begin{matrix}{{\frac{dV}{dy} = \frac{\beta \; \omega^{2}}{v}},{V = \frac{d\omega}{dy}},{{\frac{d\omega}{dV}\frac{dV}{dy}} = V},{{{\frac{\beta \; \omega^{2}}{v}\frac{d\omega}{dV}} = V};{{\frac{\beta \; \omega^{2}}{v}{d\omega}} = {VdV}}},{\frac{{\beta\omega}^{3}}{3v} = \frac{V^{2}}{2}},{V = {{{- \sqrt{\frac{2}{3}\frac{{\beta\omega}^{3}}{v}}}\frac{d\omega}{dy}} = {- \sqrt{\frac{2}{3}\frac{{\beta\omega}^{3}}{v}}}}},{\omega_{Vis} = \frac{6_{V}}{\beta y^{2}}}} & (27.0)\end{matrix}$

In order to develop a wall-function formulation the turbulent channelflow analysis previously developed for the energy dissipation rateEq.(22.0) is used. However, before that is used, the process links c andw variables by using the definition of the corresponding eddy viscosity.

$\begin{matrix}{{V_{T} = {\frac{k}{\omega} = {C_{\mu}\frac{k^{2}}{ɛ}}}}{\omega = \frac{ɛ}{C_{\mu}k}}} & (28.0)\end{matrix}$

With this relationship Eq.(22.0) can be written in terms of w.

$\begin{matrix}{\omega_{Log} = {\frac{u_{\tau}}{C_{\mu}^{1/2}\kappa \; y} = \frac{u_{\tau}}{\beta^{*\; {1/2}}\kappa \; y}}} & (29.0)\end{matrix}$

FIG. 4 shows how numerical solutions on a turbulent channel flow predictboth limits presented in Eqs.(27) and (29). It is desired to develop anequation that merges both limits.

$\begin{matrix}{\omega_{Hyb} = \frac{u_{\tau}}{\beta^{*{1/2}}\kappa \; {y\left\lbrack {1 - e^{- \frac{\beta u_{\tau}y}{6\beta^{*{1/2}}v\; \kappa}}} \right\rbrack}}} & (30.0)\end{matrix}$

FIG. 5 shows that an analytical hybrid function, e.g., Eq.30.0, is ableto predict very closely, the numerical calculations. FIG. 5 shows effectof blending (ω_(hyb)) of the ancillary relationships for ω at theviscous sublayer y+<5 (ω_(V)) and logarithmic layer y+>25 (ω_(L)) whichproduces the correct ancillary relationship as a function of y+.

The numerical calculations cannot predict with high accuracy the stiffvariation of ω in the viscous sublayer if the mesh resolution is notaround y⁺<0.1, e.g., in FIG. 7, when the near-wall resolution is notsufficiently fine in gradation. The numerical calculations shown incircles used a near-wall resolution of y+=0.05. The results show thatthe value of ω at the first cell does not fall on the theoretical valueexpected (ω_(v)) and induces an incorrect distribution of ω on theremaining grid points located at the inner layer (see FIG. 7), where thevalue of ω (solid circles) are above the ω_(v) distribution. Therefore,results show mesh dependency that results because diffusiondiscretization cannot properly resolve the steep gradients of ω as shownbelow.

In the viscous sublayer for y⁺<2 Eq. 26 needs to be accuratelydiscretized. The solution of Eq. 26 requires enforcing Eq. 30 at thefirst cell away from the wall Y1, (FIG. 8). However, the diffusiondiscretization does not adequately recover the analytical gradient of ωat the wall.

$\begin{matrix}{{\omega_{v} = \frac{c}{y^{2}}},{\frac{\partial\omega_{v}}{\partial y} = {\frac{{- 2}c}{y^{3}} = \frac{{- 2}\omega_{v}}{y}}}} & \left( {32.a} \right)\end{matrix}$

It is assumed that by specifying the value of ω at the first cell awayfrom the wall, will be sufficient to capture the diffusion flux for thefirst element that needs the diffusion flux. However, the first cellsaway from the wall are not integrated, and the values are prescribedusing Eq. 30. The finite volume discretization used for the diffusioncannot reproduce the steep variation of ω as is shown below.

$\begin{matrix}{\frac{\partial\omega_{v}^{d}}{\partial y} = {\frac{\omega_{2} - \omega_{1}}{y_{2} - y_{1}} = {\frac{c}{y_{2} - y_{1}}\left\lbrack {\frac{1}{y_{2}^{2}} - \frac{1}{y_{1}^{2}}} \right\rbrack}}} & \left( {32.b} \right)\end{matrix}$

Here by substituting the viscous sublayer behavior of ω, and after, somealgebraic manipulation of Eq. 32.b, shows that the gradient of ω at theelement face is incorrect:

$\begin{matrix}{\frac{\partial\omega_{v}^{d}}{\partial y} = {- {\frac{2\omega_{f}}{\left( \frac{y_{2} + y_{1}}{2} \right)}\left\lbrack \frac{\left( \frac{y_{2} + y_{1}}{2} \right)^{4}}{y_{1}^{2}y_{2}^{2}} \right\rbrack}}} & (33.0)\end{matrix}$

That is, the numerical discretization using the prescribed values of ωwill always over predict the diffusion flux, which is why the slope inFIG. 9 is steeper.

However, if the mesh resolution is y⁺<0.1, the first grid point is closeenough to the wall to allow the solution to eventually asymptoticallyapproach the correct distribution of ω, even with the incorrect fluxinduced by Eq. 33.

Thus, the overall solution is not changed by any further meshrefinements. However, if the first grid point is located y+>0.1, Eq.33will introduce a larger numerical error that inhibits the solution toasymptotically approach the correct distribution of ω before ω changestowards its log-region value. This will produce errors in thecalculations that are mesh dependent, and are more noticeable when thenear-wall resolution is found between 0.1<y⁺<2, as shown in FIG. 6.

It is possible to analytically derive the equation for the diffusion ofω, and therefore a correction to the diffusion flux can be introduced(Eq.34) to account for the error in Eq. 33.

$\begin{matrix}{\frac{\partial\omega_{v}^{f}}{\partial y} = {\frac{\partial\omega_{v}^{d}}{\partial y}\left\lbrack \frac{y_{1}^{2}y_{2}^{2}}{\left( \frac{y_{2} + y_{1}}{2} \right)^{4}} \right\rbrack}} & (34.0)\end{matrix}$

The corrected value of ω is used in the TKE equation preventing overproduction of TKE associated when ω is damped. It is very important tonote that Eq.34 is only correct for y⁺<3.5 where Eq. 33 is valid. Inorder to show the improvements in accuracy by using Eq. 34, numericalcalculations of channel flows showing the predicted velocity profile andfriction factor using Eq. 30 are presented below.

FIG. 9 shows typical conventional results that occur when near wallresolutions of y+<3 are used, showing that the results are meshdependent. The lack of accuracy on friction factor and velocitydistribution are evident. That is, there are large errors introduced formeshes with near-wall resolution of Y+0.5 and 2.5 (see FIG. 9). Pluralresolutions are plotted but only selective ones indicated by a referenceline for clarity.

FIG. 10, however shows that when Eq. 34 is used, the mesh dependency iseliminated, and the accuracy is substantially improved, as shown.Improvements in accuracy on friction factor and velocity distributionwith Eq. (34.0) are quite evident. That is, the large errors shown inFIG. 9, which are introduced for meshes with near-wall resolution ofY+0.5 and 2.5 are eliminated for these resolutions, and accurate resultsare to be expected demonstrating the importance of Eq. 34 to improve theaccuracy of calculations. Plural resolutions are plotted but onlyselective ones indicated by a reference line for clarity.

While errors associated with viscous discretization Eq. (33.0) can becorrected with Eq. 34.0, unfortunately there is another errorencountered when the near-wall resolution is located at the bufferlayer, where the value of ω neither satisfies the viscous sublayer northe logarithmic layer.

FIG. 11 shows that Eq. 30 is able to predict very closely, the exactsolution of the ω's partial differential equation (Eq.25.b), in both theinner and log regions, and while for Eq. 30 the numerical values at thebuffer layer are very close to the numerical solutions from Eq. 25.b,the values at the buffer layer are under-predicted.

FIG. 12 illustrates the performance of the new boundary conditions of(ω_(hyb)) when the first grid point away from the wall is located on thebuffer layer. This behavior is expected since at the buffer layer(5<y+<25) neither ω_(v) nor ω_(L) are valid behavior. The velocityprofile shows an under prediction of the log-layer, which is associatedto an increase in friction velocity from what it is expected. As aconsequence, the wall-shear is larger, thus increasing the frictionfactor. The increase in the friction factor is due to a lower value of ωas predicted by Eq. 34 at the first cell when it is located in thebuffer layer, as FIG. 10 shows. The lower value of ω induces largervalues of TKE and as a consequence larger wall-shear. Plural resolutionsare plotted but only selective ones indicated by a reference line forclarity.

In instances, both the viscous sublayer wv and the logarithmic layervalue of ω_(L) will under-predict the values that are needed at thebuffer layer, and thus neither of them nor any average of them would besufficient.

Therefore, a buffer layer correction that increase the values of ω onlyat the buffer layer needs to be developed. In order to provide such acorrection function Eqs. 27, 29, and 30 will be used in the followingway:

$\begin{matrix}{{f_{\omega_{Vis}} = \frac{\omega_{Vis}}{\omega_{Hyb}}},\mspace{14mu} {f_{\omega_{Log}} = \frac{\omega_{Log}}{\omega_{Hyb}}},{f_{blend} = {f_{\omega_{Vis}} + f_{\omega_{Log}}}}} & (35.0)\end{matrix}$

Equation 35 is plotted on FIG. 13, it shows an increase of about 30% inthe buffer layer, it also exhibit asymptotic behavior that extendsbeyond the buffer layer, which can be problematic. The new ω boundarycondition equation ω_(hyb) that is valid for viscous layer, bufferlayer, and logarithmic region can be written as:

ω′_(Hyb) =f _(blend)ω_(Hyb)   (36.0)

FIG. 14 shows improvement at the buffer layer when Eq. 30 is correctedusing Eq. 35. The improvement as illustrated in and at the buffer layeris substantial. Here the behavior of ω inside the inner layer isuniversal for wall-function theory, thus is Re independent provided theflow is turbulent. These relationships are developed under theassumption of equilibrium and as such can only be expected to work onsimulated flows that satisfy the underlying assumptions that lead to thederivation of these equations.

FIG. 14, illustrates an ω relationship that applies the blendingfunction on ω_(hyb) to produce a function referred as ω_(HCorr) that isaccurate throughout the wall (all y+, viscous sublayer, buffer layer,and logarithmic layer). While, it could be argued that the blendingfunction results would not apply when the flow is not in equilibrium,these are valid as applied to the whole wall-function approach. However,the assumptions, in general, provide near-wall modeling for flows whoseconditions depart from the idealizations applied to derive the boundaryconditions equations. Therefore, Eq. 36 which recovers the viscoussublayer, logarithmic layer, and provides corrections for meshindependent results at the viscous sublayer and buffer layer can beregarded as the best boundary condition treatment for ω and it is validas long as the wall-function approach is valid.

Equation 35 has an asymptotic behavior that permeates well into theviscous sublayer and the logarithmic region. Since correction of Eq. 30need only apply in the buffer layer, the blending function can belimited to act in the buffer layer to prevent affecting the values atthe inner layer and log layer which do not need to corrected, as inequation Eq.36.1.

$\begin{matrix}{{\omega_{Hyb}^{\prime} = {f\omega_{Hyb}}},\mspace{14mu} {f = \left\{ \begin{matrix}{{f = f_{blend}},{\ {5.5 < y^{+} < {16}}}} \\{{f = 1.0},{\ {y^{+} < {5.5}}},\ {y^{+} > {16}}}\end{matrix} \right.}} & (36.1)\end{matrix}$

Results using the viscous sublayer correction (Eq. 34) and the bufferlayer correction (Eq. 36.1) improves significantly the accuracy at thenear-wall behavior as shown in FIG. 15.

FIG. 15 illustrates results applying the universal boundary conditionω_(HCorr), which shows significant improvement on results in the bufferlayer. Plural resolutions are plotted but only selective ones indicatedby a reference line for clarity. While the buffer layer results improvesubstantially, the predicted friction factor is also in better agreementand the large errors observed in FIG. 11 are substantially removed.

Embodiments of the subject matter and the functional operationsdescribed in this specification can be implemented in digital electroniccircuitry, tangibly-embodied computer software or firmware, computerhardware (including the structures disclosed in this specification andtheir structural equivalents), or in combinations of one or more ofthem. Embodiments of the subject matter described in this specificationcan be implemented as one or more computer programs (i.e., one or moremodules of computer program instructions encoded on a tangiblenon-transitory program carrier for execution by, or to control theoperation of, data processing apparatus). The computer storage mediumcan be a machine-readable storage device, a machine-readable storagesubstrate, a random or serial access memory device, or a combination ofone or more of them.

The term “data processing apparatus” refers to data processing hardwareand encompasses all kinds of apparatus, devices, and machines forprocessing data, including by way of example, a programmable processor,a computer, or multiple processors or computers. The apparatus can alsobe or further include special purpose logic circuitry (e.g., an FPGA(field programmable gate array) or an ASIC (application-specificintegrated circuit)). In addition to hardware, the apparatus canoptionally include code that creates an execution environment forcomputer programs (e.g., code that constitutes processor firmware, aprotocol stack, a database management system, an operating system, or acombination of one or more of them).

A computer program, which can also be referred to or described as aprogram, software, a software application, a module, a software module,a script, or code, can be written in any form of programming language,including compiled or interpreted languages, or declarative orprocedural languages, and it can be deployed in any form, including as astand-alone program or as a module, component, subroutine, or other unitsuitable for use in a computing environment. A computer program may, butneed not, correspond to a file in a file system. A program can be storedin a portion of a file that holds other programs or data (e.g., one ormore scripts stored in a markup language document, in a single filededicated to the program in question, or in multiple coordinated files(e.g., files that store one or more modules, sub-programs, or portionsof code)). A computer program can be deployed so that the program isexecuted on one computer or on multiple computers that are located atone site or distributed across multiple sites and interconnected by adata communication network.

The processes and logic flows described in this specification can beperformed by one or more programmable computers executing one or morecomputer programs to perform functions by operating on input data andgenerating output. The processes and logic flows can also be performedby, and apparatus can also be implemented as, special purpose logiccircuitry (e.g., an FPGA (field programmable gate array) or an ASIC(application-specific integrated circuit)).

Computers suitable for the execution of a computer program can be basedon general or special purpose microprocessors or both, or any other kindof central processing unit. Generally, a central processing unit willreceive instructions and data from a read-only memory or a random accessmemory or both. The essential elements of a computer are a centralprocessing unit for performing or executing instructions and one or morememory devices for storing instructions and data. Generally, a computerwill also include, or be operatively coupled to receive data from ortransfer data to, or both, one or more mass storage devices for storingdata (e.g., magnetic, magneto-optical disks, or optical disks), however,a computer need not have such devices. Moreover, a computer can beembedded in another device (e.g., a mobile telephone, a personal digitalassistant (PDA), a mobile audio or video player, a game console, aGlobal Positioning System (GPS) receiver, or a portable storage device(e.g., a universal serial bus (USB) flash drive), to name just a few).

Computer-readable media suitable for storing computer programinstructions and data include all forms of non-volatile memory on mediaand memory devices, including by way of example semiconductor memorydevices (e.g., EPROM, EEPROM, and flash memory devices), magnetic disks(e.g., internal hard disks or removable disks), magneto-optical disks,and CD-ROM and DVD-ROM disks. The processor and the memory can besupplemented by, or incorporated in, special purpose logic circuitry.

To provide for interaction with a user, embodiments of the subjectmatter described in this specification can be implemented on a computerhaving a display device (e.g., a CRT (cathode ray tube) or LCD (liquidcrystal display) monitor) for displaying information to the user and akeyboard and a pointing device (e.g., a mouse or a trackball) by whichthe user can provide input to the computer. Other kinds of devices canbe used to provide for interaction with a user as well; for example,feedback provided to the user can be any form of sensory feedback (e.g.,visual feedback, auditory feedback, or tactile feedback) and input fromthe user can be received in any form, including acoustic, speech, ortactile input. In addition, a computer can interact with a user bysending documents to and receiving documents from a device that is usedby the user, for example, by sending web pages to a web browser on auser's device in response to requests received from the web browser.

Embodiments of the subject matter described in this specification can beimplemented in a computing system that includes a back-end component(e.g., as a data server), or that includes a middleware component (e.g.,an application server), or that includes a front-end component (e.g., aclient computer having a graphical user interface or a web browserthrough which a user can interact with an implementation of the subjectmatter described in this specification), or any combination of one ormore such back-end, middleware, or front-end components. The componentsof the system can be interconnected by any form or medium of digitaldata communication (e.g., a communication network). Examples ofcommunication networks include a local area network (LAN) and a widearea network (WAN) (e.g., the Internet).

The computing system can include clients and servers. A client andserver are generally remote from each other and typically interactthrough a communication network. The relationship of client and serverarises by virtue of computer programs running on the respectivecomputers and having a client-server relationship to each other. In someembodiments, a server transmits data (e.g., an HTML page) to a userdevice (e.g., for purposes of displaying data to and receiving userinput from a user interacting with the user device), which acts as aclient. Data generated at the user device (e.g., a result of the userinteraction) can be received from the user device at the server.

While this specification contains many specific implementation details,these should not be construed as limitations on the scope of anyinvention or on the scope of what can be claimed, but rather asdescriptions of features that can be specific to particular embodimentsof particular inventions. Certain features that are described in thisspecification in the context of separate embodiments can also beimplemented in combination in a single embodiment. Conversely, variousfeatures that are described in the context of a single embodiment canalso be implemented in multiple embodiments separately or in anysuitable subcombination. Moreover, although features can be describedabove as acting in certain combinations and even initially claimed assuch, one or more features from a claimed combination can in some casesbe excised from the combination, and the claimed combination can bedirected to a subcombination or variation of a subcombination.

Similarly, while operations are depicted in the drawings in a particularorder, this should not be understood as requiring that such operationsbe performed in the particular order shown or in sequential order, orthat all illustrated operations be performed, to achieve desirableresults. In certain circumstances, multitasking and parallel processingcan be advantageous. Moreover, the separation of various system modulesand components in the embodiments described above should not beunderstood as requiring such separation in all embodiments, and itshould be understood that the described program components and systemscan generally be integrated together in a single software product orpackaged into multiple software products.

Particular embodiments of the subject matter have been described. Otherembodiments are within the scope of the following claims. For example,the actions recited in the claims can be performed in a different orderand still achieve desirable results. As one example, the processesdepicted in the accompanying figures do not necessarily require theparticular order shown, or sequential order, to achieve desirableresults. In some cases, multitasking and parallel processing can beadvantageous.

What is claimed is:
 1. A computer-implemented method for simulatingfluid flow about a simulated physical object, the method comprising:receiving by one or more computing systems, a model of a simulationspace that includes a mesh defining a representation of the physicalobject in the simulation space, with the mesh comprising plural cellshaving resolutions to account for surfaces of the physical object;determining boundary conditions for a specific energy dissipation rateof a k-Omega turbulence fluid flow model of the fluid flow, by:determining a cell center of a cell in the mesh; computing by the one ormore computing systems from the cell center distance and fluid flowvariables a value of the specific energy dissipation rate for aturbulent flow that is valid for a viscous layer, buffer layer, andlogarithmic region of a boundary defined in the simulation space.
 2. Themethod of claim 1 wherein for a cell located at a position y+<3 of theboundary where y is a cell at the boundary, the method furthercomprises: applying by the one or more computing systems, a buffer layercorrection factor as a boundary condition for the energy dissipationrate.
 3. The method of claim 1 wherein determining the boundaryconditions further comprises: applying by the one or more computingsystems, a buffer layer correction factor as a boundary condition forthe energy dissipation rate for a cell located at a position y+<3 of theboundary where y is a cell at the boundary, and the correction factor isgiven according to:ω′_(Hyb) =f _(blend)ω_(Hyb) where ω′_(Hyb) is the correction factorf_(blend) is a blending function and ω_(Hyb) is a viscus layercorrection function
 4. The method of claim 1, further comprises:applying by the one or more computing systems, a viscous sublayercorrection factor as a boundary condition for the energy dissipationrate.
 5. The method of claim 1 wherein determining the boundaryconditions further comprises: applying by the one or more computingsystems, a viscous sublayer correction factor as a boundary conditionfor the energy dissipation rate, with the viscous sublayer correctionfactor is given according to:$\frac{\partial\omega_{v}^{f}}{\partial y} = {\frac{\partial\omega_{v}^{d}}{\partial y}\left\lfloor \frac{y_{1}^{2}y_{2}^{2}}{\left( \frac{y_{2} + y_{1}}{2} \right)^{4}} \right\rfloor}$${where}\mspace{14mu} \frac{\partial\omega_{v}^{f}}{\partial y}$ isthe correction factor, y₁ ² is the cell at location 1 and y₂ ² is thecell at position
 2. 6. The method of claim 3 wherein determining theboundary conditions further comprises: applying by the one or morecomputing systems, a viscous sublayer correction factor as a boundarycondition for the energy dissipation rate, with the viscous sublayercorrection factor is given according to:$\frac{\partial\omega_{v}^{f}}{\partial y} = {\frac{\partial\omega_{v}^{d}}{\partial y}\left\lfloor \frac{y_{1}^{2}y_{2}^{2}}{\left( \frac{y_{2} + y_{1}}{2} \right)^{4}} \right\rfloor}$${where}\mspace{14mu} \frac{\partial\omega_{v}^{f}}{\partial y}$ isthe correction factor, y₁ ² is the cell at location 1 and y₂ ² is thecell at position
 2. 7. The method of claim 1, further comprising:accessing the k-Omega model; initializing the accessed k-Omega modelwith the determined boundary conditions; and executing the initializedk-Omega model to simulated the fluid flow about the simulated physicalobject.
 8. The method of claim 1, further comprising: accessing thek-Omega turbulence fluid flow model, with the k-Omega turbulence fluidflow model, including a first partial differential equation to determineturbulent kinetic energy of the fluid flow; and a second partialdifferential equation to determine the specific energy dissipation rateof the fluid flow in the simulation space.
 9. The method of claim 1,further comprising: determining whether the location is at a bufferlayer; and when at a buffer layer, applying a correction that increasethe values of energy dissipation rate only at the buffer layer by:applying a blending function that acts on the values of energydissipation rate at the buffer layer and prevents the blending functionto affect values at the viscous layer of the boundary defined in thesimulation space.
 10. The method of claim 9 wherein applying thecorrection further comprises: applying a blending function that acts onthe values of energy dissipation rate at the buffer layer and preventsthe blending function to affect values at the viscous layer of theboundary defined in the simulation space.
 11. A system for simulating aphysical process flow about a simulated physical object, the systemcomprising: one or more processor devices; memory operatively coupled tothe one or more processor devices; storage media storing a computerprogram comprising instructions to cause the system to: receive a modelof a simulation space that includes a mesh defining a representation ofthe physical object in the simulation space, with the mesh comprisingplural cells having resolutions to account for surfaces of the physicalobject; determine boundary conditions for a specific energy dissipationrate of a k-Omega turbulence fluid flow model of the fluid flow, byinstructions to cause the system to: determine a cell center of a cellin the mesh; and compute from the cell center distance and fluid flowvariables a value of the specific energy dissipation rate for aturbulent flow that is valid for a viscous layer, buffer layer, andlogarithmic region of a boundary defined in the simulation space. 12.The system of claim 11, further configured to: determine that a cell islocated at a position y+<3 of the boundary where y is a cell at theboundary; and apply a buffer layer correction factor as a boundarycondition for the energy dissipation rate, with the correction factorgiven according to:ω′ _(Hyb) =f _(blend)ω_(Hyb) where ω′_(Hyb) is the correction factorf_(blend) is a blending function and ω_(Hyb) is a viscus layercorrection function
 13. The system of claim 11, further configured to:apply a viscous sublayer correction factor as a boundary condition forthe energy dissipation rate, with the viscous sublayer correction factoris given according to:$\frac{\partial\omega_{v}^{f}}{\partial y} = {\frac{\partial\omega_{v}^{d}}{\partial y}\left\lfloor \frac{y_{1}^{2}y_{2}^{2}}{\left( \frac{y_{2} + y_{1}}{2} \right)^{4}} \right\rfloor}$${where}\mspace{14mu} \frac{\partial\omega_{v}^{f}}{\partial y}$ isthe correction factor, y₁ ² is the cell at location 1 and y₂ ² is thecell at position
 2. 14. The system of claim 12, further configured to:apply a viscous sublayer correction factor as a boundary condition forthe energy dissipation rate, with the viscous sublayer correction factoris given according to:$\frac{\partial\omega_{v}^{f}}{\partial y} = {\frac{\partial\omega_{v}^{d}}{\partial y}\left\lfloor \frac{y_{1}^{2}y_{2}^{2}}{\left( \frac{y_{2} + y_{1}}{2} \right)^{4}} \right\rfloor}$${where}\mspace{14mu} \frac{\partial\omega_{v}^{f}}{\partial y}$ isthe correction factor, y₁ ² is the cell at location 1 and y₂ ² is thecell at position
 2. 15. The system of claim 12, further configured to:access the k-Omega model; initialize the accessed k-Omega model with thedetermined boundary conditions; and execute the initialized k-Omegamodel to simulated the fluid flow about the simulated physical object.16. A computer program product for simulating a physical process, thecomputer program product tangibly stored on a non-transitory computerreadable storage medium, the computer program product comprisinginstructions to cause a system to: receive a model of a simulation spacethat includes a mesh defining a representation of the physical object inthe simulation space, with the mesh comprising plural cells havingresolutions to account for surfaces of the physical object; determineboundary conditions for a specific energy dissipation rate of a k-Omegaturbulence fluid flow model of the fluid flow, by instructions to causethe system to: determine a cell center of a cell in the mesh; andcompute from the cell center distance and fluid flow variables a valueof the specific energy dissipation rate for a turbulent flow that isvalid for a viscous layer, buffer layer, and logarithmic region of aboundary defined in the simulation space.
 17. The computer programproduct of claim 16, further comprising instructions to cause the systemto: determine that a cell is located at a position y+<3 of the boundarywhere y is a cell at the boundary; and apply a buffer layer correctionfactor as a boundary condition for the energy dissipation rate, with thecorrection factor given according to:ω′_(Hyb) =f _(blend)ω_(Hyb) where ω′_(Hyb) is the correction factorf_(blend) is a blending function and ω_(Hyb) is a viscus layercorrection function
 18. The computer program product of claim 16,further comprising instructions to cause the system to: apply a viscoussublayer correction factor as a boundary condition for the energydissipation rate, with the viscous sublayer correction factor is givenaccording to:$\frac{\partial\omega_{v}^{f}}{\partial y} = {\frac{\partial\omega_{v}^{d}}{\partial y}\left\lfloor \frac{y_{1}^{2}y_{2}^{2}}{\left( \frac{y_{2} + y_{1}}{2} \right)^{4}} \right\rfloor}$${where}\mspace{14mu} \frac{\partial\omega_{v}^{f}}{\partial y}$ isthe correction factor, y₁ ² is the cell at location 1 and y₂ ² is thecell at position
 2. 19. The computer program product of claim 18,further comprising instructions to cause the system to: apply a viscoussublayer correction factor as a boundary condition for the energydissipation rate, with the viscous sublayer correction factor is givenaccording to:$\frac{\partial\omega_{v}^{f}}{\partial y} = {\frac{\partial\omega_{v}^{d}}{\partial y}\left\lfloor \frac{y_{1}^{2}y_{2}^{2}}{\left( \frac{y_{2} + y_{1}}{2} \right)^{4}} \right\rfloor}$${where}\mspace{14mu} \frac{\partial\omega_{v}^{f}}{\partial y}$ isthe correction factor, y₁ ² is the cell at location 1 and y₂ ² is thecell at position
 2. 20. The computer program product of claim 16,further comprising instructions to cause the system to: access thek-Omega model; initialize the accessed k-Omega model with the determinedboundary conditions; and execute the initialized k-Omega model tosimulated the fluid flow about the simulated physical object.